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Well-being adjusted life years (WALYs)
Suppose we accept we can use life satisfaction (LS) scores to measure happiness. What next? One, straightforward option would be to measure LS (and other subjective well-being (SWB) metrics) impacts directly in randomised controlled trials. If we know the costs of a programme, we could then establish how much it costs to produce one ‘life satisfaction point-year’ or ‘LSP’ - equivalent to increasing life satisfaction for one person by one point on a 10 point scale for a year. This method is structurally similar to assessing cost per Quality-Adjusted Life Year (QALY) - which effective altruists are generally familiar with and I won’t go into - except QALYs are measured on a 0-1 scale whereas LS is on a 0-10 scale. 
Table 1. How adult life satisfaction (0-10) is affected by current circumstances (BHPS) (cross-section) (Clark et al. 2018, p199)
WALYs > QALYs
I expect many effective altruists will welcome the idea of using LSPs instead of QALYs, as many already accept that, in principle, we need a measure of ‘well-being adjusted life-years’ (WALYs).[1] QALYs capture health, and as I noted at the start, not only is health not all that matters, we will still need a common currency that allows health and non-health outcomes to be traded-off against one another, and a non-arbitrary method to determine the value of outcomes in this currency. LSPs could partially or fully fulfil the role of being the WALY metric. For those who think happiness is the only intrinsic good, LSPs should be sufficient - unless and until a better measure of happiness can be found. Those that value goods other than happiness will, presumably, value happiness to some extent, and inasmuch as they do, LSPs will be one aspect of WALYs they need to consider alongside other goods.[2] Data collection and RCTs Data from randomised controlled trials using LS will not always be available. Where it is not, an alternate way to determine how different outcomes affect LS is to rely on data from large population surveys. Using a multivariate regression analysis that controls for different circumstances, researchers can then estimate the strength of the correlations between LS and various other factors. Table 1 from Clark et al. (2018, p199) contains the results of such an analysis both for the impact a given change has on an individual’s LS and that which it has on others. This information can be used to make inferences about the expected LS effect of a given outcome without requiring a randomised controlled trial, at least if it’s straightforward to measure the outcome, as it is in cases of unemployment for example. In other cases the relationship between life satisfaction and other measures, such as particular health metrics, it will need to be established so other metrics can be converted in LS scores. Some of this work been done: see Layard (2016) for such a table converting LS scores into both other SWB measure and various health metrics. Three remarks on the results in the table that will be relevant again shortly:
While it is already possible to estimate the LS effect of many outcomes, if effective altruists want to use SWB data to assess effectiveness, they should encourage researchers - most obviously those working in global development - to collect it alongside other variables. This only requires quickly surveying individuals at the start and end of an impact assessment. This generates extra work, but also allows direct measurement of the outcome that is (presumably) of most interest. Endnotes
[1] MacAskill explicitly states this in Doing Good Better (2015). [2] Suppose someone thought there are two intrinsic goods, happiness and autonomy. They would need a measure of autonomy - autonomy-adjusted life years? (AALYs) - and they would need to set up a conversion rate between LSPs and AALYs to determine which outcome did the most good on the composite measure. |